Language Model Optimization For In-Domain Application

ABSTRACT

Systems and methods are provided for optimizing language models for in-domain applications through an iterative, joint-modeling approach that expresses training material as alternative representations of higher-level tokens, such as named entities and carrier phrases. From a first language model, an in-domain training corpus may be represented as a set of alternative parses of tokens. Statistical information determined from these parsed representations may be used to produce a second (or updated) language model, which is further optimized for the domain. The second language model may be used to determine another alternative parsed representation of the corpus for a next iteration, and the statistical information determined from this representation may be used to produce a third (or further updated) language model. Through each iteration, a language model may be determined that is further optimized for the domain.

BACKGROUND

Automatic speech recognition (ASR) uses language models for determiningplausible word sequences for a given language or application domain. Theprevailing approaches to ASR typically use discrete words (n-grams) asthe basic units of language modeling. But these approaches are limitedto relatively short memory spans and an inability to efficientlygeneralize from limited training data.

Attempts to mitigate these weaknesses include casting named entities asa class, in a class-based language model (LM), where the classes may beautomatically inferred or manually defined, and in some cases, employingcommon cohesive word strings for improving post-recognition tasks, suchas machine translation, information extraction, and languageunderstanding. However, such methods are inefficient, contextuallyunaware, or otherwise do not preserve the simplicity and scalability ofn-gram language models. Moreover, approaches using larger-span units ormulti-word units in addition to discrete words promise improvement,especially for applications that are domain-constrained.

SUMMARY

This Summary is provided to introduce a selection of concepts in asimplified form that are further described below in the DetailedDescription. This Summary is not intended to identify key features oressential features of the claimed subject matter, nor is it intended tobe used as an aid in determining the scope of the claimed subjectmatter.

Embodiments of the invention are directed towards systems and methodsfor determining and optimizing language models by way of iterativeprocesses that express training material as alternative representationsof higher-level units, such as named entities and carrier phrases. Inparticular, embodiments automatically determine representations or“parses” of a training corpus as multi-word named entities, phrases,including carrier phrases, and words. These representations are used toproduce an LM optimized for the corpus domain, which is class based interms of named entities and contains the phrases as independentpseudo-words. Thus, embodiments of the invention provide a singleunified and unsupervised language-modeling approach that combines thediscriminative power of words, the generalizing power of classes, andthe context modeling power of common phrases. Such LMs offer significantimprovements in speech recognition and understanding, machinetranslation, and other tasks where LMs are used.

As will be further described, in one embodiment, an iterativeoptimization algorithm is employed for expressing a word-level corpus oftraining material in terms of “tokens” (unit n-grams) representingwords, entities, or phrases. An LM is used to parse the training corpusinto sequences of tokens. From the parsed representations of thetraining corpus, token n-gram probabilities are determined, therebyupdating (or retraining) the LM. A maximum likelihood solution may bedetermined with respect to all possible parses that can account forinput sequences. The updated LM is used to determine alternative parsedrepresentations of the corpus for a next iteration, and theprobabilities determined from these alternative parsed representationsare used to update (retrain) the LM again. Thus, with each iteration,alternative parsed representations of the training corpus are determinedusing the LM, and the LM is updated to reflect the token n-gramprobabilities determined from the parsed representations determined inthat iteration. In this manner, each iteration results in an LM that isfurther optimized for the corpus domain. Some embodiments furtherinclude optimizing entity definitions in a similar manner.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention is illustrated by way of example and notlimitation in the accompanying figures in which like reference numeralsindicate similar elements and in which:

FIG. 1 is a block diagram of an example system architecture in which anembodiment of the invention may be employed;

FIG. 2 depicts aspects of the iterative approach to parsing a trainingcorpus in accordance with an embodiment of the invention;

FIG. 3 depicts a flow diagram of a method for optimizing a languagemodel for a domain by iterative parsing of a training corpus, inaccordance with an embodiment of the present invention;

FIG. 4 depicts a flow diagram of a method for optimizing a languagemodel for a domain, in accordance with an embodiment of the presentinvention;

FIG. 5 depicts a flow diagram of a method for optimizing a languagemodel for a domain by iterative parsing of a training corpus, inaccordance with an embodiment of the present invention; and

FIG. 6 is a block diagram of an exemplary computing environment suitablefor use in implementing an embodiment of the present invention.

DETAILED DESCRIPTION

The subject matter of the present invention is described withspecificity herein to meet statutory requirements. However, thedescription itself is not intended to limit the scope of this patent.Rather, the inventors have contemplated that the claimed subject mattermight also be embodied in other ways, to include different steps orcombinations of steps similar to the ones described in this document, inconjunction with other present or future technologies. Moreover,although the terms “step” and/or “block” may be used herein to connotedifferent elements of methods employed, the terms should not beinterpreted as implying any particular order among or between varioussteps herein disclosed unless and except when the order of individualsteps is explicitly described.

Aspects of the technology described herein are directed towards systems,methods, and computer storage media for, among other things, determiningand optimizing language models for in-domain applications. Inembodiments of the invention, language models may be optimized throughan iterative, joint-modeling approach that expresses in-domain trainingmaterial as alternative representations of higher-level units, such asnamed entities and carrier phrases. In particular, embodimentsautomatically determine representations or “parses” of a training corpusas multi-word named entities, phrases including carrier phrases, andwords. These representations are used to produce an LM optimized for thetraining corpus domain, which is class based in terms of named entitiesand contains the phrases as independent pseudo-words. Thus, embodimentsof the invention provide a single unified and unsupervisedlanguage-modeling approach by combining the words, phrases, and entitiesinto a language model wherein the words, phrases, and named entitiescoexist in the same probability space.

Optimized LMs created by embodiments of the invention offer significantimprovements in speech recognition and understanding, machinetranslation, and other tasks where LMs are used. For example, these LMsare typically more compact, thereby improving efficiency. Additionally,they are capable of providing efficient decoding even for classes withlarge numbers of entities, unsupervised (or lightly supervised, in someembodiments) extraction of entities and carrier phrases, alleviating theneed for large tagged corpora, and a mechanism for incorporating domainconstraints during recognition, thereby improving the quality of inputfor downstream processing such as understanding. Further, such modelsfacilitate staying updated as specific instances of entity-types change.For example, if a new movie comes out, only the definition of the movieentity class may need updating; the LM is capable of recognizing themovie and information related to the movie. Still further, these modelsfacilitate analysis of the nature of the application domain, such asdetermining typical contexts of the entities (e.g., what words orphrases is the movie entity-type typically surrounded by).

As used herein, the term “token” (or unit) means one or more words, aphrase, or an entity. For example, consider the sentence, “I would liketo follow Angelina Jolie on Twitter.” (Capitalization or italics may beincluded in some instances herein for reading ease.) The phrase “I wouldlike to” could include four or less tokens (e.g., one token for eachword, tokens representing 2 or more words, or one token representing allfour words). Similarly, “Angelina Jolie” could be two word tokens (forthe words “Angelina” and “Jolie”), one entity token, or one phrase token(representing the phrase “Angelina Jolie”). A phrase token associatedwith an entity may be referred to as a carrier phrase. As used herein,the term “parse,” as a noun, refers to a sequence of one or more tokensthat represents a corpus.

Accordingly, at a high level, an iterative optimization algorithm isemployed for expressing a word-level corpus of training material interms of “tokens” (or unit n-grams) representing words, entities, orphrases, in an embodiment of the invention. An LM is used to parse thetraining corpus into sequences of these tokens. From these parsedrepresentations of the training corpus, token n-gram probabilities aredetermined, thereby updating (retraining) the LM. In one embodiment, amaximum likelihood solution is determined with respect to the parses.The updated LM is used to determine alternative parsed representationsof the corpus for a next iteration, and the probabilities determinedfrom these alternative parsed representations are used to update(retrain) the LM again.

With each iteration, alternative parsed representations of the trainingcorpus are determined using the LM, and the LM is updated to reflect thetoken n-gram probabilities determined from the parsed representationsdetermined in that iteration. In this way, the same sequences of wordsfrom the training corpus may be modeled as, for example, part of acarrier phrase in one representation, separate words in anotherrepresentation, or a named entity in yet another representation. Eachiteration results in an LM that is further optimized for the domain ofthe training corpus. Some embodiments further include optimizing theentity definitions in a similar manner, as further described herein. Inan embodiment, the iterations proceed until convergence or until the LMis deemed satisfactory, at which point the LM may be provided, or thefinal corpus representation may be used to train an entity-class-basedlanguage model for the application domain, with common carrier phrasesencoded as new pseudo-words.

For example, consider the word-level text, “i'd like to see movie up inthe air in amc sixteen and then go home,” which represents an in-domaincorpus, for purposes of the example. For the sake of clarity, thisexample corpus includes only one sentence, but it is contemplated thatcorpora used by embodiments of the invention may comprise many words orsentences. One embodiment of the invention might represent this corpusas “i'd+like+to see movie <movie> in <theater> and then go+home.” Thisexample parse of the corpus represents the original word-level corpus asa sequence of tokens including phrase tokens “i'd+like+to” and“go+home;” word tokens “see,” “movie,” “in,” “and,” and “then;” andentity tokens for a movie entity-type and a theatre entity-type. Usingthis example parse for training an LM results in a class-based modelthat has classes but also includes phrases encoded as new pseudo-words.

As described above, an embodiment of the invention provides aniterative, unsupervised approach, referred to herein as an algorithm orlearner, for determining and optimizing an LM for in-domainapplications. The learner can be unsupervised because it performsautomatically and does not require supervised training. For example, inone embodiment, the only input for the learner includes an in-domaincorpus of words and some definitions of entities. The learner isiterative because it can perform a series of iterations to determine anoptimized LM, wherein each iteration uses an LM produced in a previousiteration to determine parsed representations of the corpus.

Thus, a goal of the learner is to produce a representation of theoriginal corpus in terms of tokens (parsed words, phrases, andentities). From the representation of tokens, statistics associated withthe tokens and words within the tokens may be determined and used toupdate or retrain the LM, in an embodiment. The statistics may includefrequency, occurrence, proximity, and/or other probabilities associatedwith the tokens and words within the tokens. The updated LM, which maybe considered a new or retrained LM, may be used then to re-express theoriginal corpus into alternative representations of tokens, which maythen be used to generate an updated LM in a subsequent iteration. Eachiteration produces an LM that is further tailored to the domain of thecorpus. In this sense, the learner performs as an ExpectationsMaximization algorithm, wherein determining parses corresponds to theexpectation step and generating the updated LM, by estimatingprobabilities associated with the tokens of the parse, corresponds tothe maximization step.

Turning now to FIG. 1, a block diagram is provided showing aspects of anexample system architecture suitable for implementing an embodiment ofthe invention and designated generally as system 100. It should beunderstood that this and other arrangements described herein are setforth only as examples. Thus, system 100 represents only one example ofa suitable computing system architecture. Other arrangements andelements (e.g., user devices, data stores, etc.) can be used in additionto or instead of those shown, and some elements may be omittedaltogether for the sake of clarity. Further, many of the elementsdescribed herein are functional entities that may be implemented asdiscrete or distributed components or in conjunction with othercomponents, and in any suitable combination and location. Variousfunctions described herein as being performed by one or more entitiesmay be carried out by hardware, firmware, and/or software. For instance,some functions may be carried out by a processor executing instructionsstored in memory.

Among other components not shown, system 100 includes network 115communicatively coupled to one or more user devices (e.g., items 102 and104), storage 106, and language model trainer 120. The components shownin FIG. 1 may be implemented using one or more computing devices, suchas computing device 600 described in connection to FIG. 6. Network 115may include, without limitation, one or more local area networks (LANs)and/or wide area networks (WANs). Such networking environments arecommonplace in offices, enterprise-wide computer networks, intranets,and the Internet. It should be understood that any number of userdevices, storage components, and language model trainers may be employedwithin the system 100 within the scope of the present invention. Eachmay comprise a single device or multiple devices cooperating in adistributed environment. For instance, language model trainer 120 may beprovided via multiple devices arranged in a distributed environment thatcollectively provide the functionality described herein. Additionally,other components not shown may also be included within the networkenvironment.

Example system 100 includes user devices 102 and 104, which may compriseany type of user device capable of receiving input from a user. Forexample, in one embodiment, user devices 102 and 104 may be the type ofcomputing device described in relation to FIG. 6 herein. By way ofexample and not limitation, a user device may be embodied as a personaldata assistant (PDA), a mobile device, a laptop, a tablet, remotecontrol, entertainment system, vehicle computer system, embedded systemcontroller, appliance, consumer electronic device, or other electronicsdevice capable of receiving input from a user. The input may be receivedby one of many different modalities, such as by way of example and notlimitation, voice or sound, text, touch, click, gestures, the physicalsurroundings of the user, or other input technologies described inconnection to FIG. 6. For instance, a user may utilize a search engineto input a query, intending to receive information highly relevant tothe query. Or a user may use voice commands with a gaming system,television, etc. All of these forms of input, as well as others notspecifically mentioned herein, are contemplated to be within the scopeof the present invention. In some embodiments, user input and user inputlibraries from user devices 102 and 104 may be stored in storage 106.

Example user devices 102 and 104 are included in system 100 to providean example environment wherein language models created by embodiments ofthe invention may be used by one or more user devices 102 and 104.Although, it is contemplated that aspects of the learner algorithmdescribed herein may operate on one or more user devices 102 and 104, itis also contemplated that some embodiments of the invention do notinclude user devices. For example, aspects of the learner algorithm maybe embodied on a server or in the cloud. Further, although FIG. 1 showstwo example user devices 102 and 104, a user may be associated with onlyone user device or more than two devices.

Storage 106 may store training material; entity definitions; informationabout parsed representations of corpora; statistical information, whichmay be determined from statistics determining component 126; and LMs,which may be produced by language model generating component 128. In anembodiment, storage 106 comprises a data store (or computer datamemory). Further, although depicted as a single data store component,storage 106 may be embodied as one or more data stores or may be in thecloud.

By way of example and not limitation, training material stored onstorage 106 may include textual information collected, derived, or minedfrom one or more sources such as user queries, SMS messages, webdocuments, electronic libraries, books, user input libraries, orartificially collected or created samples, for example. Trainingmaterial may be utilized and stored based on characteristics of alanguage model to be optimized. In one embodiment, training materialcomprises a collection of in-domain words, sentences or phrases, and maybe referred to herein as a corpus.

Storage 106 may also store information about parsed representations ofcorpora (i.e., parses). In some embodiments, corpora parses are storedas a lattice structure, as described in connection to parsing component124. Information about the parses may include tokens created from words,entities, or phrases of a corpus; statistics associated with the tokens;and tags, which may identify the token type. In some embodiments, tokensare tagged by parsing component 124 to represent a type of sequences ofwords, such as an entity-type (also referred to herein as an entityclass). Tags facilitate naming and identifying a span or words thatlikely belong together. Thus, the example provided above, “i'd like tosee movie up in the air in amc sixteen and then go home,” may berepresented as “i'd+like+to see movie <movie>in <theater> and thengo+home.” The span of words “up-in-the-air” may be tagged and replacedby entity-type <movie>. Similarly, “amc sixteen” may be tagged andreplaced by entity-type <theater>. The sequence “i'd+like+to” may betagged as a carrier phrase.

Entity definitions include information about one or more entitiesassociated with entity-types. As used herein, the term “entity” isbroadly defined to include any type of item, including a concept orobject that has potential relationships with other items. For example,an entity might be the movie “Life is Beautiful,” the director “RobertoBenigni,” or the award “Oscar.” Collections of entities carrying similarsyntactic and/or semantic meaning comprise entity types (e.g. movietitles, songs, time expressions etc.). Furthermore, related entity typescan be organized into domains. For instance, within the movie domain,the movie “Life is Beautiful” is directed by “Roberto Benigni,” and themovie also won an Oscar.

In one embodiment, entity definitions comprise explicitly enumeratedentity instances associated with an entity-type, such as a weighted listof alternatives (e.g., word tries) for a particular entity-type. Forexample, for the actor entity-type, entity definitions might include alist of actor names. In some embodiments, as described below, each actorname (or each instance of an entity) has a corresponding probability(statistical data), which may correspond to how popular (frequentlyoccurring) the actor is, based on the training material. Thus, aparticular instance of an entity for an entity-type may be determinedbased on the list of entities and probabilities.

Entity definitions may also comprise implicitly defined instances ofentity-types. In particular, for certain entity-types, it is notefficient to explicitly enumerate all possible instances of theentity-type. For example, while all (or most) actors could be explicitlyincluded in a definition for the actor entity-type, it is not efficientto enumerate all possible phone numbers, temporal information, such asdates and times, or other combinatorial entity-types. Therefore, in someembodiments, these entities may be implicitly defined by combinatorialmodels that can provide the entity definition. For example, a finitestate machine (FSM) or similar model may be used. As with explicitlyenumerated entity definitions, in some embodiments, implicitlyenumerated entity instances have a corresponding probability(statistical data), which may correspond to how frequently occurring theentity instance is within the training corpus.

In some embodiments, the entity definitions may be updated throughvarious iterations of the learner, as further described in connection toFIG. 3. For example, initially the list of actors included in the entitydefinitions for the actor entity-type may have an equal weighting(representing probabilities or statistical data), but it may bedetermined over several iterations that certain actors (e.g., Brad Pitt)are more common than other actors (e.g., Terry O'Quinn). Thus,statistical data associated with Brad Pitt would be updated to reflectthis. Similarly, for the temporal entity-types, initially all instancesof an entity-type for times may have an equal probability. But it may bedetermined that certain times are more popular; for example, in anapplication domain related to office meetings, a meeting scheduled at 3pm is probably more popular (frequently occurring) than one scheduled at11 pm. Accordingly, the corresponding statistical data for the entitydefinitions may be updated to reflect this.

Language model trainer 120 comprises accessing component 122, parsingcomponent 124, statistics determining component 126, and LM generatingcomponent 128. In one embodiment, language model trainer 120 may beimplemented on one or more devices, such as user devices 102 and 104, ona server or backend computing system (not shown), or on a distributedplatform (not shown) in the cloud. Language model trainer 120 isconfigured to determine and optimize LMs for in-domain applications.Language model trainer 120 and its components may reference a pluralityof text data and/or information stored on storage 106 to implementembodiments of the invention, including the methods described inconnection to FIGS. 3-5.

The accessing component 122 is configured to access corpora of textualinformation, entity definitions, statistics information, and/or languagemodels. Textual information (or text data) may comprise one or moreletters, words, or symbols belonging to one or more languages, and maycomprise language model training materials such as described inconnection to storage component 106. In an embodiment, accessingcomponent 122 accesses one or more entity definitions and an in-domaincorpus of words. The one or more words may include stand-alone words,words comprising phrases, words in sentences, or a combination thereof.The accessed information might be stored in storage 106, memory, orother storage. Further, the information may be accessed remotely and/orlocally.

Parsing component 124 is configured to generate alternative parsedrepresentations of a corpus of textual information. Each parse includesa series of non-overlapping tokens representing the corpus. Each of thealternative parses represents the corpus using a different formation oftokens. For example, using the corpus “i'd like to see up in the air,”one alternative parse would be “i'd+like+to”, “see”, “up+in+the+air”.Another alternative parse would be “i'd+like”, “to+see”, “up+in”, “the”,“air”. Each parse may have statistical data associated with it, whichmay be determined by statistics determining component 126 and used toproduce a new (or update an existing) LM by LM generating component 128.

Parsing component 124 uses statistical data associated with an LM todetermine the alternative parses of the corpus. With each iteration ofthe learner algorithm, alternative parsed representations of the corpusare determined based on an LM, which may have been generated based onstatistics associated with the parses from the previous iteration. Inthis way, the same sequences of words from the corpus may be modeleddifferently, such as a part of a carrier phrase in one representation,separate words in another representation, or a named entity in yetanother representation. In some embodiments, new parses are determinedwith each iteration, while in other embodiments all possible parses (orthe most probable parses based on the statistical information) aredetermined initially, and then a subset of these are used in eachiteration by the learner algorithm.

In an embodiment, parsing component 124 determines a “lattice” datastructure of nonlinear sequences of corpus elements. The lattice datastructure is a directed graph providing a compact representation of anumber of alternative parses. Each path through the lattice produces adifferent parse of the corpus, and each path is associated with aprobability. One particular path (e.g., “i'd+like+to” “see”“up+in+the+air”) may be determined to have a higher probability thananother path (e.g., “i'd+like” “to+see” “up+in” “the” “air”).

In one embodiment, parsing component 124 may be embodied using trellis,where each position in a sentence corresponds to a trellis slice andstates of the slide (contexts) represent: context n-gram history:previous (n-1) tokens in the parse and their word-lengths (e.g., phrase“i+live+in” followed by a 2-word long instance of entity STATE), and/orcurrently active entity, the state with this entity and its currentlength (if any; e.g., second word of phrase “i+live+in”). In someembodiments, parsing component 124 stores parse information in storage106. Additional details regarding parsing are described in connection toFIG. 2.

Statistics determining component 126 is configured to determinestatistical data such as the frequency, occurrence, proximity or worddistance between words or elements in the corpus or token, and/or otherprobabilities associated with the tokens making up the parses andelements within the tokens, such as words or entity information. Thisincludes estimating the n-gram level probabilities for tokens used foran LM and determining the maximum likelihood estimates for the n-gramprobabilities. In particular, statistics determining component 126determines statistics used to produce a new or updated LM including (a)probabilities of a specific token, given a token history (i.e., tokensfollowing each other), and (b) probabilities within tokens, such as theprobability of a word(s) given a token (such as the probability of aspecific instance given an entity-type). Statistical data determined bystatistics determining component 126 also includes entity statisticaldata, or probabilities associated with entity definitions, as describedabove.

In an embodiment, using a training corpus and entity definitions,statistics determining component 126 may determine a first set ofstatistical data for an initial (or first) LM. This first set ofstatistical data may comprise a first set of entity statisticsassociated with the entity definitions and statistical informationdetermined from the training corpus. From the first set of statisticaldata, LM generating component 128 may produce a first LM, as describedbelow. In one embodiment, the first LM is a unigram LM (unigram in termsof tokens). Parsing component 124 may use the first LM to determine afirst set of alternative parses comprising sequences of tokensrepresenting the training corpus. Statistics determining component 126is configured to determine a second set of statistical data for a secondLM based on the token information from the first set of alternativeparses, as described above. For example, statistical data may becollected or obtained by analyzing each of the alternative parses in thefirst set of alternative parses. In an embodiment, statisticsdetermining component 126 is also configured to determine a second setof entity statistics from the first set of alternative parses. Thesecond set of statistical data and/or the second set of entitystatistical data may be used by LM generating component 128 to produce asecond LM. In one aspect, the second LM may be considered an updated,optimized version of the first LM.

In further embodiments, the second LM may be used by parsing component124 to determine a second set of alternative parses, from whichstatistical data determined by statistics determining component 126 maybe used by LM generating component 128 to produce a third LM. Thisdescription illustrates the iterative nature of embodiments describedherein; where a plurality of sets of language model statistical data,sets of entity statistical data, language models, and sets ofalternative parses may be generated to continually improve the currentiteration of the language model by optimizing it for the corpus domain.

In one embodiment, entity definitions might be similarly improvedthrough iteration, whereby statistics determining component 126determines a set of entity statistical data from the set of alternativeparses, such as based on frequency (popularity) of entities, asdescribed above, or proximity to other entities or tokens.

LM generating component 128 is configured to take the statistical datadetermined by statistics determining component 126 and produce a new(updated) LM. In particular, estimating and using n-gram probabilitiesof tokens (parses) is equivalent to training and using an n-gramlanguage model, thus LM generating component 128 applies the statisticaldata corresponding to the tokens of the alternative parses as a new LM,in an embodiment.

The LM produced by LM generating component 128 from statistical dataincludes two parts: (a) a probability of a specific token, givensequences of prior tokens, and (b) a probability of a word or specificentity instance, given the token or entity-type (i.e., what is happeningwithin the tokens). For example, the first part might include theprobability of “actor” token given the sequence of tokens “I'd+like+to”and “follow” (word token), as in “I'd like to follow Brad Pitt.” Thesecond part, which relates to the entity definitions, might include theprobability of “Brad Pitt” (specific instance) given “actors”(entity-type). Thus, from the example above, we might expect a trainedLM to have a higher probability of Brad Pitt than Terry O'Quinn.

In embodiments, the LM produced by LM generating component 128 is ajoint model because it may be considered to comprise aspects of threemodels: (1) Entity models (i.e., entity definitions), which may beinitialized as predefined weighted lists or grammars, but can also betuned to the learner algorithm, as described above; (2) carrier phrasemodels, which may be estimated from the corpus based on word n-gramoccurrence statistics (for example, the more often a particular n-gramoccurs relative to occurrences of its ingredient elements (words), thebetter chance of it becoming a carrier phrase. Similarly, co-occurrencestatistics of the n-grams left and right context also contribute to thisdetermination.); and (3) word level language models. For example, bytraining an LM on top of the training corpus (either based on anoriginal word-level representation or alternative parses determined atintermediate stages), the probability of one token given a number ofother tokens may be estimated. Combined into a single generativeprobabilistic model, the three component models can effectively tag thecorpus in terms of higher-level tokens.

In an embodiment, during the first iteration, LM generating component128 may generate an initial LM based on statistical data determined fromthe training corpus and a set of entity definitions, as describedherein. From this LM, a first set of parsed representations of thecorpus may be determined, as described above, from which statisticaldata is determined and used to produce an updated LM. With eachiteration of the learner algorithm, LM generating component 128 producesan updated LM that is further optimized for the domain of the trainingcorpus. In some embodiments, the updated LM updates or replaces the LMfrom a previous generation. This updating or replacement of a previouslanguage model with an updated language model may continue for multipleiterations of the learner algorithm, for example, until convergence or aset of statistical data associated with the model reaches anoptimization threshold, and/or for a predetermined number of iterations.

In some embodiments, only the most recently updated or optimizedlanguage model and/or entity definitions are retained and applied by thelearner algorithm for the next iteration. In alternative embodiments,more than one updated language model and/or entity definitions might beretained and/or applied by the learner. Further, models and/ordefinitions might be retained to evaluate the collected statisticsand/or diagnostically analyze the learner as implemented by someembodiments of the invention.

Accordingly, language model trainer 120 and its components determine andoptimize language models for in-domain applications. The followingillustrates one embodiment in regards to FIG. 1. By way of example only,in an embodiment, we start off accessing a corpus from storage 106 thatcomprises a certain number of sentences. During each iteration of thelearner algorithm, each of the sentences is represented by manydifferent alternative parses, as determined by parsing component 124using a language model. Each parse has a probability, determined bystatistics determining component 126, which is reflected in the languagemodel used to generate the parse.

In particular, each sentence may be turned into a number of differentparses (different representations); with each one assigned aprobability. For example, suppose the corpus is “I'd like to followAngelina Jolie on Twitter.” Then using the same LM, parsing component124 may determine the alternative parses such as the following: a firstalternative parse comprising separate words (i.e., each token in theparse is a single word from the corpus); a second alternativecomprising: “I'd+like+to” (as a phrase token), “follow” (as a wordtoken), “Angelina Jolie” (as an entity token), “and” (as a word), “on”(as a word), “Twitter” (as a word); and a third alternative comprising:“I'd+like+to” (as a phrase), “follow+Angelina+Jolie” (as a phrase), “on”(as a word), “Twitter” (as a word). (For the sake of clarity, only threeexample parses are provided in this example, but embodiments may includemany additional parses.)

Thus, the same language model is used by parsing component 124 toproduce several alternative parses. As described above, each parse has aprobability, which may be determined by statistics determining component126. More specifically, each parse has a posterior probability, and sodo tokens within a parse; for instance, probability of “Angelina Jolie”as an instance of an actor entity token, or as a phrase; or individualprobabilities of words “Angelina” and “Jolie”.

The alternative parses and their associated probabilities (as weights)are used by LM generating component 128 to produce a new LM, which maybe considered an update to the previous LM. As might be expected, forearlier iterations, the probabilities associated with the parses mayappear unreasonable. (For example, during early iterations, the token“Jolie+on” might have the same probability as “Angelina+Jolie”.) But asthe iterations continue, the parses become as might be expected. Forexample, if “I'd+like+to+follow” appears often in the context of anactor/actress, then the iterative training process discovers this.

In some embodiments, only a limited number of parses are produced witheach iteration, after the first iteration, based on the probabilitiesassociated with the parses. For example, in one embodiment, all possibleparses of a sentence may be implicitly ranked at each iteration byprobability so that the more probable parses come to the top and can beexplicitly enumerated (n-best). Moreover, in practice, parses in thelater iterations may not be present in the alternative parses determinedin earlier iterations.

Because the learner is unsupervised, the most probable parses may bedetermined automatically, in some embodiments. Based on the associatedprobabilities, the learner may also determine whether a sequence ofwords is a phrase token or entity token, and if it is an entity, thenwhat is the likely entity-type (or entity class). For example, using thecorpus “I'd like to follow Angelina Jolie on Twitter”, the learner maydetermine a representation “I'd+like+to follow <actor>”. Usingstatistics determining component 126, the learner may compare theprobabilities of “Angelina” as a word, “Angelina Jolie” as a phrase, or“Angelina Jolie” as an instance of an entity in the actor entity class.

The following illustrates another example of generating alternativeparses in regards to FIG. 1. By way of example only, in an embodiment, atraining corpus of textual information is accessed by accessingcomponent 122. The corpus includes the words: “I'd like to see up in theair at amc sixteen and then go home.” Using an initial language model,parsing component 124 determines a set of alternative parses. For eachalternative parse, statistics determining component 126 determinesprobabilities associated with each parse, which may be used as weightsby LM generating component 128 to produce a new LM. In the nextiteration, the new LM may be used by parsing component 124 to determineanother set of alternative parses.

After a number of iterations, from statistical data associated with theLM for a current iteration, parsing component 124 may determine a parsethat includes the tokens “I'd+like+to” and “go+home” with associatedprobabilities indicating that these tokens are likely common phrases.Similarly, from the statistical data associated with entity definitionsof the LM, parsing component 124 may recognize and parse tokens“up+in+the+air” and “amc sixteen” as entity tokens. As described above,in an embodiment, the entity tokens may be tagged and replaced by anentity class. For example, “up+in+the+air” may be tagged and replaced byentity-type <movie>, and “amc sixteen” tagged and replaced with theentity-type <theatre>.

As described above, once a set of alternative parses has been determinedby parsing component 124, statistics determining component 126determines probabilities associated with the parses, includingprobabilities associated with the tokens in the parses. Over multipleiterations, the statistical data associated with alternative parses andtokens reveals most probable tokens for the corpus domain. (For example,“Angelina+Jolie on Twitter” will likely be determined as more probablethan “Angelina Jolie+on Twitter.”) Further, the statistical data maydetermine additional information, such as the probability that the movieentity-type will occur within n words of the theater entity-type or thatthe entity “up in the air” is likely to occur within m words of thephrase “I'd+like+to+see.”

The following illustrates another example of determining and optimizinga language model for an application domain, in regards to FIG. 1. Thisexample may apply to the first time that a particular training corpus isanalyzed, such that a previous iteration of a language model for thatparticular corpus is not available. By way of example only, in anembodiment, a training corpus of textual information and a set of entitydefinitions is accessed by accessing component 122. From this, aninitial LM may be determined on top of the corpus. In one embodiment,the initial LM comprises a unigram LM.

In particular, parsing component 124 may parse the corpus as sequencesof words up to n words in length (i.e., tokens of n words). These tokensmay be evaluated as candidates for phrases and entities. For example, bychecking a token against the entity definitions by counting theoccurrences of a candidate entity or phrase in the corpus. Theoccurrence counts may be compiled to form an initial set of statisticaldata, which may be used as weights for an LM. In this aspect, theinitial set of statistical data is not collected from a previous set ofalternative parses. For example, statistics determining component 126may determine that a particular phrase, such as “I'd+like+to,” occurredas a phrase in the corpus n many times, or that “Angelina+Jolie”occurred as an actor m many times and as a director k many times. In anembodiment, the number of all other actors and directors in the corpusmay be determined. The ratio of the counts for “Angelina+Jolie” as actorand as director versus the total numbers may be used as an initialprobability or weight. In this way, an initial LM may be determined.

Turning now to FIG. 2, a perspective 200 is illustratively provided thatdepicts aspects of the iterative approach of one embodiment of thelearner algorithm for parsing a training corpus. Perspective 200 shows acorpus 202 of textual information comprising sequences of words 204, anda set of entity definitions 210 and phrase definitions 212. Entitydefinitions 210 and phrase definitions 212 may include explicitly and/orimplicitly defined entities and phrases. For example, a movieentity-type might explicitly enumerate every movie title with acorresponding probability (or weight) associated with each title; whilea date, phone number, or other temporal expression may be defined by acombinatorial model, such as an FSM. The trie structure is shown initems 210 and 212 to indicate definitions of an entity-type or words orphrases that are generated out of component words.

From the corpus 202 and the entity and phrase definitions (items 210 and212), each iteration of the learner algorithm applies a language model220 at 206 to generate parsed representations of the corpus (shown asparsed data 214). Statistical data determined in part from the parseddata 214 may be applied at 216 to update the LM 220 (which may be storedin storage 208) and in some embodiments also update the entitydefinitions 210 and phrase definitions 212. For the first iteration, anLM may be trained on top of the entity and phrase definitions, such asdescribed in connection to FIG. 1. Thereafter, the LM is updated witheach iteration, based on statistical data associated with the parsedrepresentations. Iterations may continue until convergence or until themodel is deemed satisfactory, at which point the final corpusrepresentation may be used to train an entity-class-based language model(a production language model), with common carrier phrases encoded asnew pseudo-words.

Turning to FIG. 3, a flow diagram is provided illustrating one exemplarymethod 300 for determining and optimizing a language model. The languagemodel produced according to method 300 is suitable for use in anapplication domain represented by a training corpus that is used tooptimize the model.

At a high level, in one embodiment, once an initial LM is determined,such as described below, each iteration of method 300 starts with an LMthat it produced in a previous iteration. The LM reflects statisticaldata about a training corpus, which may be expressed as weights,including (a) a probability of a specific token, given sequences ofprior tokens, and (b) a probability of a word or specific entityinstance, given the token or entity-type (i.e., what is happening withinthe tokens). Given the LM, the training corpus may be parsed intoalternative representations of tokens according to the statistical datareflected in the LM. From these different representations or parses,other statistical data may be determined and used to produce a new LM,which may be considered an updated LM. Thus, for this embodiment, eachiteration of method 300 includes two steps: a parsing step, wherein thecorpus is represented in terms of sequences of tokens, and a step fordetermining a new LM from probabilities associated with the parses.

For example, consider a training corpus already represented as acollection of weighted token-level sentences, which may have beendetermined from an initial LM; for example, the sentence:“i+would+like+to travel from CITY to CITY” with a weight of 10. It isstraight forward to estimate an n-gram language model based on thistraining corpus by incrementing the count for each n-gram in the corpusby the weight of the sentence it occurred in. But consider further thateach sentence has its probability mass split among several alternativeparses. In the above case, the alternatives might include “i+wouldlike+to travel from CITY to san+francisco” with weight 3; or “i wouldlike to travel+from+boston to CITY” with weight 2. Thus, to determine anLM from these alternative parses, again the token n-grams from allalternatives are counted. However, now the counters are incremented onlyby a fraction of the original weight, which is equal to the cumulativeposterior probability of the parses containing these n-grams. Using thisnew LM, the original word-level representation of the training corpusmay be parsed to produce another collection of alternative parses foreach of its sentences; and thus the next iteration begins. Eachiteration produces an LM that is further optimized to the domain of thecorpus. In this sense, the learner performs as an ExpectationsMaximization algorithm, wherein determining a parse corresponds to theexpectation step and generating the updated LM, by estimatingprobabilities associated with the tokens of the parse, corresponds tothe maximization step.

Accordingly, method 300 begins step 310, wherein LM training material isreceived. In one embodiment, training material comprises a trainingcorpus of textual information and a set of entity definitions. Thetextual information may comprise one or more alphanumeric characters, orwords, which may be in sentences, or other sequences of alphanumericcharacter strings. In one embodiment, the training material may bereceived from one or more sources including user queries, SMS messages,web documents, electronic libraries, books, user input libraries, orartificially collected or created samples, for example. In oneembodiment, the training material defines a domain and in one embodimentis related to a structured application domain; for example, a homeentertainment system, personal device, or vehicle.

At step 320, an initial LM is determined. In one embodiment, an initialLM may be created from statistical data determined from the trainingcorpus, at a word-level, and entity definitions, as described herein.The statistical data underlying the language model creation for thefirst iteration can be initialized from word-n-gram powersets in allphrases of the word-level training corpus. In one embodiment, entitydefinitions may be seeded with weighted lists or grammars, which may beuniformly distributed, and phrases may be automatically identified inthe corpus based on their occurrence statistics, as described above.

In one embodiment of step 320, all n-grams of a certain length areextracted from the corpus. Each n-gram that constitutes a name from someentity is also counted as an observation of this entity (weighted by theprior probability of this name in this particular entity). In oneembodiment, the set of these n-grams may be then filtered with respectto a minimum observation frequency. From these accumulated counts, aunigram LM may be trained that is used to parse the data in the firstiteration.

In particular, all word sub-sequences π of length up to L are extractedfrom the training corpus. For each π, all entities c are queried forprior probability P^(prior) (π|c) and, where it is positive, the countof c is incremented by #w*P^(prior) (π|c), where w represents a sentenceof N words, w=w¹ . . . w^(N). Corpus-wide counts of π are accumulated aswell. In one embodiment, where the count exceeds a minimum threshold, πis designated a phrase token. Additionally, all regular words andclasses, regardless of their occurrence count, become tokens as well.Thereafter, a unigram LM may be trained from this statistical data asthe initial token-level LM.

In some situations, certain entity surface forms may also be commonwords or expressions; for example, the movie titles “It,” “Her,” or“Up.” To avoid having entities with these surface forms beinginstantiated in every sentence with these words, in some embodiments,these terms may be first identified, such as by using an off-the-shelflanguage model, and then banned from participating in parsing for thefirst few iterations.

At step 330, alternative parses are determined from the corpus using anLM, which for the first iteration may be the initial LM determined fromstep 320, or for subsequent iterations, the LM determined in step 340.In an embodiment, alternative parsed representations of the corpus aredetermined using the statistical data reflected in the LM, as describedherein, wherein each parse comprises a sequence of one or more tokensthat together represent the corpus.

In an embodiment, each sentence of the corpus may be turned into ndifferent parses (n different representations); with each one assigned aprobability (n-best). For example, consider a sentence of N words, w=w¹.. . w^(N) and real-valued weight L (that initially may reflectobservational counts: L(w):=#w). This sentence may be segmented in manydifferent ways according to the statistical data associated with the LM,with single- and multi-word tokens competing against each other forwords. Thus, a particular parse c^(k)=(c₁ ^(k) . . . c_(|c) ^(k) _(|)^(k)) may induce segmentation π^(k)=(π₁ ^(k) . . . π_(|c) ^(k) _(|)^(k)) that partitions the sentence into a number of word sub-sequencesπ_(i) ^(k), with each sub-sequence being a token instance c_(i) ^(k). Ajoint likelihood of the sentence and the parse may be obtained via thechain rule:

$\begin{matrix}{{P\left( {w,c^{k}} \right)} = {\prod\limits_{c_{i}^{k} \in c^{k}}^{\;}\; {{P\left( {c_{i}^{k}h_{i}^{k}} \right)}{P\left( {\pi_{i}^{k}c_{i}^{k}} \right)}}}} & (1)\end{matrix}$

The first term is an n-gram probability of token c_(i) ^(k) in tokenn-gram history h_(i) ^(k)=c_(i−n+1) ^(k) . . . c_(i−1) ^(k). The secondterm is determined by the nature of the token. For words and phrases,this probability is 1.0; for an entity class, this is the priorprobability of a particular surface form of the class. Depending on theentity nature, it may be represented by one of the following twoformalisms, as described in connection to FIG. 2. Specifically, for(weighted) lists such as personal names or movie titles, word tries maybe used. (In one embodiment, word tries are also used to represent theplurality of phrases.) For combinatorial entities, such as dates andtimes, combinatorial models, such as FSMs, may be used.

In some embodiments, only a limited number of parses are produced witheach iteration, after the first iteration, based on the probabilitiesassociated with the parses. For example, in one embodiment, all possibleparses of a sentence may be ranked at each iteration by probability sothat the more probable parses come to the top (n-best).

At step 340, an updated LM is generated. In an embodiment, the updatedLM is produced based on statistical data associated with the alternativeparsed representations determined in step 330. In particular, an n-gramlanguage model may be produced (or updated) by determining the n-gramprobabilities of tokens, such as occurrence statistics for tokenn-grams, information-theoretical criteria for carrier phrases, and/orinstance probabilities within entity-types, which may be estimated fromthe n-best parses determined in the previous step.

In one embodiment, continuing with the example introduced in step 330,maximum-likelihood (ML) estimates for the n-gram probabilities P(c|h)for token c and its token-level history h from a single sentence w, maybe determined by accumulating parse-specific observation counts:

$\begin{matrix}\begin{matrix}{{P^{ML}\left( {{ch},w} \right)} = {{\sum\limits_{k}\frac{\# \mspace{11mu} {ch}}{\# h}}_{c^{k}}{P\left( {c^{k}w} \right)}}} \\{= {{\sum\limits_{k}\frac{\# \mspace{11mu} {ch}}{\# h}}_{c^{k}}\frac{P\left( {wc^{k}} \right)}{\sum_{c}{P\left( {w,c} \right)}}}}\end{matrix} & (2)\end{matrix}$

Taking the average over an entire corpus {w_(j)} closes the expectationmaximization loop:

$\begin{matrix}\begin{matrix}{{P^{ML}\left( {ch} \right)} = {\sum\limits_{j}{{P\left( w_{j} \right)}{P^{ML}\left( {{ch},w_{j}} \right)}}}} \\{= {\sum\limits_{w}{\frac{L(w)}{\sum_{\overset{\_}{w}}{L\left( \overset{\_}{w} \right)}}{P^{ML}\left( {{ch},w} \right)}}}} \\{{= {{\sum\limits_{w}{\sum\limits_{k}{{L^{\prime}\left( {w,k} \right)}\frac{\# \mspace{11mu} {ch}}{\# h}}}}_{c^{k}}}},}\end{matrix} & (9)\end{matrix}$

where the weights of each parse may be set to:

$\begin{matrix}{{L^{\prime}\left( {w,k} \right)}:={\frac{L(w)}{\sum_{\overset{\_}{w}}{L\left( \overset{\_}{w} \right)}}*\frac{P\left( {wc^{k}} \right)}{\sum_{c}{P\left( {w,c} \right)}}}} & (4)\end{matrix}$

In an embodiment, the maximization of equation (3) may be naturallyextended to incorporate smoothing techniques, such as back-offs.Accordingly, this process is equivalent to producing an n-gram LM from anew representation of the training corpus that now comprises a weightedcollection of token-level parses of the original sentences.

At step 345, it is determined whether to continue iterating method 300.In an embodiment, the LM produced in step 340 is evaluated in order todetermine whether convergence has been reached, whether a set ofstatistical data associated with the model reaches an optimizationthreshold, or whether the model is otherwise determined satisfactorybased on the evaluation. In one embodiment, an iteration count may beemployed such that the determination as to whether to continue iteratingis based at least in part on the iteration count. If it is determinedthat another iteration is to be performed, then method 300 goes to step330, wherein the LM generated in step 340 is used to determinealternative parses of the training corpus. On the other hand, if themodel is determined to be satisfactory (e.g., if convergence isdetermined), then method 300 proceeds to step 350.

At step 350, the optimized LM is provided. In an embodiment, the LMprovided in step 350 is optimized for the domain defined or representedby the training corpus. In an embodiment, the final corpusrepresentation may be used to train an entity-class-based language modelfor the application domain, with common carrier phrases encoded as newpseudo-words.

Some embodiments of method 300 further comprise retraining thedefinitions of entities and phrases, with each iteration. In particular,in some embodiments, a second goal of the learner algorithm includesoptimizing the definitions of entities, which may be represented in theLM determined during the iteration. For example, continuing with theexample introduced in step 330, entity definitions may be updated in asimilar maximum-likelihood fashion. For entities modeled as word tries,counting is simple. If π is a particular surface form of entity token c,then its probability may be determined as:

$\begin{matrix}\begin{matrix}{{P^{ML}\left( {\pi c} \right)} = {\sum\limits_{j}{{P\left( w_{j} \right)}{P^{ML}\left( {{\pi c},w_{j}} \right)}}}} \\{= {{\sum\limits_{w}{\sum\limits_{k}{{L^{\prime}\left( {w,k} \right)}\frac{\# \left( {c,\pi} \right)}{\# c}}}}_{c^{k}}}}\end{matrix} & (5)\end{matrix}$

For combinatorial entities such as times and dates, the re-estimation issimilar, except that it may be carried out for each emanating arc (andfinal cost) of each state, in a normalized deterministic FSM. Moreover,because training corpora may be limited in size, the ML estimator fromequation (5) may suffer from over-training, in some scenarios. On theother hand, the generic entity definitions might be robust but may lackdomain specificity, in some scenarios. Therefore, in some embodiments, atrade-off may be established between the two. For example, on eachiteration t we can linearly interpolate between the posteriorprobability P_((t)) ^(ML) and prior probability P_((t)) ^(prior) suchthat:

P _((t))(π|c):=(1−λ_((t)))P _((t)) ^(ML)(π|c)+λ_((t)) P _((t))^(prior)(π|c)   (6)

with iteration-specific inertia λ_((t)) defined as:

$\begin{matrix}{\lambda_{(t)}:=\left\{ \begin{matrix}{1.0,} & {{{if}\mspace{14mu} t} < {\kappa \mspace{14mu} {or}\mspace{14mu} {Z_{(t)}(c)}} < \theta} \\{\lambda^{0.5{({t - \kappa})}},} & {{otherwise}.}\end{matrix} \right.} & (7)\end{matrix}$

For the first κ iterations, the prior probability may be determined bythe initial LM definition; after that: P_((t))^(prior)(π|c):=P_((t−1))(π|c). Parameters θ, κ, and λ may be setempirically, and Z_((t))(c) is the normalization factor:

$\begin{matrix}{{Z(c)} = {{\sum\limits_{w}{\sum\limits_{k}{{L^{\prime}\left( {w,k} \right)}\# c}}}_{c^{k}}}} & (8)\end{matrix}$

computed at iteration t.

In some embodiments of method 300, one or more thresholds may be appliedto control the size of the model. For example, thresholds may beemployed to make it harder for new items to be accepted into the model.In some embodiments, thresholds may be determined empirically or may bedetermined based in part on a particular corpus or structured domain.Three examples of such thresholds include thresholds for phrases(non-trivial sequences of words that we accept as new tokens),(n>1)-grams of tokens that include phrases, and named entitydefinitions.

In situations where a high number of long phrases exist, the model maybecome over-trained. Thus, in some embodiments, thresholds may beimposed on how many times a phrase needs to occur in the corpus in orderto be preserved for a future iteration. Because some embodiments of thelearner algorithm apply alternative weighted parses and rely onfractional observation counts, such thresholds acquire a differentmeaning. For instance, a phrase might occur in parses of 100 differentsentences but only in n-best alternatives of very low probabilities. Asa result, this phrase might not even survive occurrence threshold of1.0.

Similarly, a high number of n-grams may cause the model to becomeover-trained. Thus, in some embodiments, cut-offs may be imposedregarding how many times an n-gram of particular length needs to occurin order to be accepted into a language model. In an embodiment, highercut-offs may be imposed for n-grams with one token being a phrase.

In some embodiments, thresholds may be imposed based on named entitydefinitions, such as to purge entities whose corresponding probabilitydrops below a certain threshold. For example, as an LM becomes updatedwith each iteration of method 300, the occurrence of individualnames/transitions may be counted in the parses created by the LM andused to update the probabilities in the entity definitions. In oneembodiment, measured frequencies may be interpolated with the priorprobabilities in the spirit of Dirichlet prior distributions, whereprior probabilities have been estimated on the basis of some othercorpus of a certain size.

Turning now to FIG. 4, a flow diagram is provided illustrating oneexemplary method 400 for creating a language model optimized for adomain defined by a corpus. At step 410, a training corpus is accessed.In an embodiment, the training corpus comprises text data, for exampleone or more alphanumeric characters or words, which may be in sentences,or other sequences of alphanumeric character strings. The trainingcorpus may be accessed from a data store or may be created, mined, orderived from one or more sources such as user queries, SMS messages, webdocuments, electronic libraries, books, user input libraries, orartificially collected or created samples, for example.

At step 420, a first set of alternative parses are determined from thetraining corpus using a first LM. In an embodiment, step 420 expressesthe training corpus in terms of “tokens” (or unit n-grams) representingwords, entities, or phrases. The first LM is used to parse the trainingcorpus into one or more parses of sequences of these tokens, each parserepresenting the corpus. The one or more alternative parsedrepresentations of the corpus are determined using a first set ofstatistical data associated with the first LM. In an embodiment, thefirst LM is preexisting and may be provided, initially determined fromthe training corpus, such as described in step 320 of method 300, or maybe determined during a previous iteration of an embodiment of theiterative learner algorithm described herein. In an embodiment, thestatistical data associated with the first LM includes probabilities,which may be expressed as weights or counts, associated with tokens oreach parse. In an embodiment, the statistical data associated with thefirst LM further comprises entity statistical data.

At step 430, a second LM is determined based on the first set ofalternative parses. The second LM may comprise a new LM or update of thefirst LM that is applied in step 420. In an embodiment, the second LM isdetermined from a second set of statistical data determined from thefirst set of alternative parses. For example, from the parsedrepresentation of the training corpus determined in step 420, tokenn-gram probabilities may be determined, thereby producing a new (second)LM. In an embodiment, the second set of statistical data comprisesprobabilities of a specific token, given a token history (i.e., tokensfollowing each other), and probabilities within tokens, such as theprobability of a word(s) given a token (such as the probability of aspecific instance given an entity-type).

In an embodiment, the second LM may be used to determine a second set ofalternative parses of the corpus for a next iteration, and theprobabilities determined from this second alternative parsedrepresentation are used to produce a third LM, which is even furtheroptimized for the domain represented by the training corpus. Someembodiments of method 400 further comprise updating statisticalinformation associated with entities (entity definitions) and phrases(phrase definitions) based on the first set of alternative parsesdetermined in step 420, such as described in method 300.

It should be noted that while FIG. 4 refers to language models, sets ofalternative parses, and sets of statistical data numerically, such as afirst set, a second set, a first language model, a second languagemodel, etc., these terms should not be interpreted as being sequentialin nature. For instance, a first language model may not, in fact, be thefirst iterative version of the language model but instead a previousversion of the language model when compared to a next iteration of thelanguage model.

Turning to FIG. 5, a flow diagram is provided illustrating one exemplarymethod 500 for determining and optimizing a language model. The languagemodel produced according to method 500 is suitable for use in anapplication domain represented by a training corpus that is used tooptimize the model.

At step 510, a training corpus and a set of entity definitions arereceived. The training corpus comprises textual information; forexample, one or more alphanumeric characters or words, which may be insentences, or other sequences of alphanumeric character strings. In oneembodiment, the training corpus may be received from training materialsthat are derived, mined, or collected from one or more sources such as,by way of example and not limitation: user queries, SMS messages, webdocuments, electronic libraries, books, user input libraries, orartificially collected or created samples. Entity definitions receivedat step 510 include information about one or more entities associatedwith entity-types, which may be explicitly or implicitly defined, andcorresponding statistical information. For example, in an embodiment,each defined entity has a corresponding probability, which may bepredefined or based on the frequency of occurrence in the trainingcorpus (or another corpus). In an embodiment, the probabilities areinitially equal for entities defined in the set of entity definitions.

At step 520, an initial language model is determined. In an embodiment,an initial LM may be produced from statistical data determined from thetraining corpus and statistical data of the entity definitions, asdescribed herein. For example, in one embodiment of step 520, n-grams ofa certain length may be extracted from the training corpus. Each n-gramthat constitutes an instance of an entity-type is also counted as anobservation of this entity (weighted by the prior probability of thisinstance in this particular entity-type). In one embodiment, the set ofthese n-grams may be then filtered with respect to a minimum observationfrequency. From these accumulated counts, a unigram LM may be trainedthat is used to parse the data in the first iteration. In oneembodiment, the initial LM may be determined as described at step 320 ofmethod 300 (FIG. 3).

At step 530, alternative parses are determined from the corpus using alanguage model, which for the first iteration may be the initial LMdetermined from step 520, or for subsequent iterations, the LM generatedat step 550. In an embodiment, alternative parsed representations of thecorpus are determined using the statistical data reflected in the LM, asdescribed herein, wherein each parse comprises a sequence of one or moretokens that together represent the corpus. In some embodiments, theparsing operation performed in step 530 is similar to embodimentsdescribed in connection to step 330 of method 300 (FIG. 3).

At step 540, updated entity definitions are determined. Step 540comprises updating (or retraining) the statistical data in the set ofentity definitions, and in some embodiments, step 540 also comprisespruning the entities included in the set of entity definitions. Forexample, as described herein, entity definitions maximum-likelihoodfashion, such as described in method 300. In an embodiment, thestatistical data in the set of entity definitions is updated based onthe occurrences of entities or proximity of entities to various tokensin the parses determined in step 530.

At step 550, an updated LM is generated. In an embodiment, the updatedLM is produced based on statistical data associated with the alternativeparsed representations determined in step 530 and updated entitydefinitions determined at step 540. In particular, in an embodiment, ann-gram language model may be produced (or updated) by determining then-gram probabilities of tokens, such as occurrence statistics for tokenn-grams, information-theoretical criteria for carrier phrases, and/orinstance probabilities within entity-types, which may be estimated fromthe n-best parses determined in step 530. In some embodiments, the LMgenerating operation performed in step 550 is similar to the embodimentsdescribed in connection to step 340 of method 300 (FIG. 3).

At step 555, it is determined whether to continue iterating method 500.In an embodiment, the LM produced in step 550 is evaluated in order todetermine whether convergence has been reached, whether a set ofstatistical data associated with the model reaches an optimizationthreshold, or whether the model is otherwise determined satisfactorybased on the evaluation. In one embodiment, an iteration count may beemployed such that the determination as to whether to continue iteratingis based at least in part on the iteration count. If it is determinedthat another iteration is to be performed, then method 500 proceeds tostep 530, wherein the LM generated in step 550 is used to determinealternative parses of the training corpus. On the other hand, if themodel is determined to be satisfactory (such as if convergence isdetermined), then method 500 proceeds to step 560.

At step 560, the optimized LM is provided. In an embodiment, the LMprovided in step 560 is optimized for a domain defined or represented bythe training corpus. In an embodiment, the final corpus representationmay be used to train an entity-class-based language model for theapplication domain, with common carrier phrases encoded as newpseudo-words.

Accordingly, we have described various aspects of technology directed tosystems and methods for improving language models for determining andoptimizing language models for a domain. In particular, throughembodiments of an iterative joint-modeling learner algorithm, a set ofalternative parses of a corpus are determined and used to produce (orupdate) an LM, which may be used to parse the corpus into another set ofalternative parses, which may be used to produce a new (or updated) LM.Thus, with each iteration an LM is determined that is further optimizedfor the corpus domain.

It is understood that various features, sub-combinations, andmodifications of the embodiments described herein are of utility and maybe employed in other embodiments without reference to other features orsub-combinations. Moreover, the order and sequences of steps shown inthe example methods 300, 400, and 500 are not meant to limit the scopeof the present invention in any way and, in fact, the steps may occur ina variety of different sequences within embodiments hereof. Suchvariations and combinations thereof are also contemplated to be withinthe scope of embodiments of the invention.

Having described various embodiments of the invention, an exemplarycomputing environment suitable for implementing embodiments of theinvention is now described. With reference to FIG. 6, an exemplarycomputing device is provided and referred to generally as computingdevice 600. The computing device 600 is but one example of a suitablecomputing environment and is not intended to suggest any limitation asto the scope of use or functionality of the invention. Neither shouldthe computing device 600 be interpreted as having any dependency orrequirement relating to any one or combination of componentsillustrated.

Embodiments of the invention may be described in the general context ofcomputer code or machine-useable instructions, includingcomputer-useable or computer-executable instructions, such as programmodules, being executed by a computer or other machine, such as apersonal data assistant, a smartphone, a tablet PC, or other handhelddevice. Generally, program modules, including routines, programs,objects, components, data structures, and the like, refer to code thatperforms particular tasks or implements particular abstract data types.Embodiments of the invention may be practiced in a variety of systemconfigurations, including handheld devices, consumer electronics,general-purpose computers, more specialty computing devices, etc.Embodiments of the invention may also be practiced in distributedcomputing environments where tasks are performed by remote-processingdevices that are linked through a communications network. In adistributed computing environment, program modules may be located inboth local and remote computer storage media including memory storagedevices.

With reference to FIG. 6, computing device 600 includes a bus 610 thatdirectly or indirectly couples the following devices: memory 612, one ormore processors 614, one or more presentation components 616, one ormore input/output (I/O) ports 618, one or more I/O components 620, andan illustrative power supply 622. Bus 610 represents what may be one ormore busses (such as an address bus, data bus, or combination thereof).Although the various blocks of FIG. 6 are shown with lines for the sakeof clarity, in reality, these blocks represent logical, not necessarilyactual, components. For example, one may consider a presentationcomponent such as a display device to be an I/O component. Also,processors have memory. The inventors hereof recognize that such is thenature of the art and reiterate that the diagram of FIG. 6 is merelyillustrative of an exemplary computing device that can be used inconnection with one or more embodiments of the present invention.Distinction is not made between such categories as “workstation,”“server,” “laptop,” “handheld device,” etc., as all are contemplatedwithin the scope of FIG. 1 and with reference to “computing device.”

Computing device 600 typically includes a variety of computer-readablemedia. Computer-readable media can be any available media that can beaccessed by computing device 600 and includes both volatile andnonvolatile media, removable and non-removable media. By way of example,and not limitation, computer-readable media may comprise computerstorage media and communication media. Computer storage media includesboth volatile and nonvolatile, removable and non-removable mediaimplemented in any method or technology for storage of information suchas computer-readable instructions, data structures, program modules, orother data. Computer storage media includes, but is not limited to, RAM,ROM, EEPROM, flash memory or other memory technology, CD-ROM, digitalversatile disks (DVDs) or other optical disk storage, magneticcassettes, magnetic tape, magnetic disk storage or other magneticstorage devices, or any other medium which can be used to store thedesired information and which can be accessed by computing device 600.Computer storage media does not comprise signals per se. Communicationmedia typically embodies computer-readable instructions, datastructures, program modules, or other data in a modulated data signalsuch as a carrier wave or other transport mechanism and includes anyinformation delivery media. The term “modulated data signal” means asignal that has one or more of its characteristics set or changed insuch a manner as to encode information in the signal. By way of example,and not limitation, communication media includes wired media, such as awired network or direct-wired connection, and wireless media, such asacoustic, RF, infrared, and other wireless media. Combinations of any ofthe above should also be included within the scope of computer-readablemedia.

Memory 612 includes computer storage media in the form of volatileand/or nonvolatile memory. The memory may be removable, non-removable,or a combination thereof. Exemplary hardware devices include solid-statememory, hard drives, optical-disc drives, etc. Computing device 600includes one or more processors 614 that read data from various entitiessuch as memory 612 or I/O components 620. Presentation component(s) 616presents data indications to a user or other device. Exemplarypresentation components include a display device, speaker, printingcomponent, vibrating component, and the like.

The I/O ports 618 allow computing device 600 to be logically coupled toother devices, including I/O components 620, some of which may be builtin. Illustrative components include a microphone, joystick, game pad,satellite dish, scanner, printer, wireless device, etc. The I/Ocomponents 620 may provide a natural user interface (NUI) that processesair gestures, voice, or other physiological inputs generated by a user.In some instances, inputs may be transmitted to an appropriate networkelement for further processing. An NUI may implement any combination ofspeech recognition, touch and stylus recognition, facial recognition,biometric recognition, gesture recognition both on screen and adjacentto the screen, air gestures, head and eye tracking, and touchrecognition associated with displays on the computing device 600. Thecomputing device 600 may be equipped with depth cameras, such asstereoscopic camera systems, infrared camera systems, RGB camerasystems, and combinations of these, for gesture detection andrecognition. Additionally, the computing device 600 may be equipped withaccelerometers or gyroscopes that enable detection of motion. The outputof the accelerometers or gyroscopes may be provided to the display ofthe computing device 600 to render immersive augmented reality orvirtual reality.

Many different arrangements of the various components depicted, as wellas components not shown, are possible without departing from the scopeof the claims below. Embodiments of the present invention have beendescribed with the intent to be illustrative rather than restrictive.Alternative embodiments will become apparent to readers of thisdisclosure after and because of reading it. Alternative means ofimplementing the aforementioned can be completed without departing fromthe scope of the claims below. Certain features and subcombinations areof utility and may be employed without reference to other features andsubcombinations and are contemplated within the scope of the claims.

Accordingly, in one aspect, an embodiment of the invention is directedto one or more computer-readable media having computer-executableinstructions embodied thereon that, when executed by a computing systemhaving a processor and memory, cause the computing system to perform amethod for optimizing a language model for a domain. The method includes(a) receiving training material for training a language model, thetraining material including a corpus of one or more words and (b)determining a language model based on the training material. The methodalso includes (c) utilizing the language model to determine a set ofalternative parses of the corpus, (d) determining statistical data basedon the set of alternative parses determined in step (c), and (e) basedon the statistical data determined in step (d), updating the languagemodel. The method further includes (f) determining whether the languagemodel is satisfactory, and (g) based on the determination of whether thelanguage model is satisfactory: if the language model is determined tobe satisfactory, then providing the language model, and if the languagemodel is determined not to be satisfactory, then repeating steps (c)through (g).

In another aspect, one or more computer-readable media havingcomputer-executable instructions embodied thereon are provided that,when executed by a computing system having a processor and memory, causethe computing system to perform a method for optimizing a language modelfor a domain performed by a computing device having a processor and amemory. The method includes accessing a training corpus, the trainingcorpus comprising one or more words, and applying a first language modelto determine a first set of alternative parses of the training corpus,each parse including one or more tokens. The method also includesdetermining a first set of statistical data associated with the firstset of alternative parses, and generating a second language model basedon the first set of statistical data. In some embodiments, the methodfurther includes applying the second language model to determine asecond set of alternative parses of the training corpus, determining asecond set of statistical data associated with the second set ofalternative statistical parses, and generating a third language modelbased on the second set of statistical data.

In yet another aspect, an embodiment of the invention is directed to amethod for optimizing a language model for a domain performed by acomputing device having a processor and a memory. The method includesreceiving a training corpus comprising one or more words, receiving aset of entity definitions comprising a weighted list of one or moreentities, and determining a first language model based on the trainingcorpus and the set of entity definitions. The method also includes, fora number of iterations, each iteration using an iteration languagemodel: (a) utilizing the iteration language model to determine a set ofalternative parses of the corpus, (b) determining updated weights forthe set of entity definitions, (c) generating an updated language modelfrom the set of alternative parses determined in step (a) and from theset of updated weights determined in step (b), and (d) determining alanguage model evaluation, wherein the iteration language model is thefirst language model for the first iteration, and wherein the iterationlanguage model is the updated language model determined in step (c) foreach subsequent iteration; and wherein the number of iterations isdetermined based on the language model evaluation.

What is claimed is:
 1. One or more computer-readable media havingcomputer-executable instructions embodied thereon, that, when executedby a computing system having a processor and memory, cause the computingsystem to perform a method for optimizing a language model for a domain,the method comprising: (a) receiving training material for training alanguage model, the training material including a corpus of one or morewords; (b) determining a language model based on the training material;(c) utilizing the language model to determine a set of alternativeparses of the corpus; (d) determining statistical data based on the setof alternative parses determined in step (c); (e) based on thestatistical data determined in step (d), updating the language model;(f) determining whether the language model is satisfactory; and (g)based on the determination of whether the language model issatisfactory: (i) if the language model is determined to besatisfactory, providing the language model; and (ii) if the languagemodel is determined not to be satisfactory, repeating steps (c) through(g).
 2. The computer-readable media of claim 1, wherein the languagemodel is determined as satisfactory where it has achieved convergence.3. The computer-readable media of claim 1, wherein the training materialfurther includes a set of entity definitions.
 4. The computer-readablemedia of claim 1, wherein the training material further includes a setof entity definitions including a weighted list of entities, and whereindetermining statistical data includes determining updated weights ofentities in the set of entity definitions.
 5. The computer-readablemedia of claim 1, wherein the training material is determined from oneor more user query logs, SMS messages, web documents, electroniclibraries, books, user input libraries, or created samples.
 6. The oneor more computer-readable media of claim 1, wherein one or morethresholds is applied to control the size of the updated language model.7. The computer-readable media of claim 1, wherein determiningstatistical data based on the set of alternative parses includesdetermining a maximum-likelihood solution associated with thealternative parses.
 8. One or more computer-readable media havingcomputer-executable instructions embodied thereon, that, when executedby a computing system having a processor and memory, cause the computingsystem to perform a method for optimizing a language model for a domainperformed by a computing device having a processor and a memory, themethod comprising: accessing a training corpus, the training corpuscomprising one or more words; applying a first language model todetermine a first set of alternative parses of the training corpus, eachparse including one or more tokens; determining a first set ofstatistical data associated with the first set of alternative parses;and generating a second language model based on the first set ofstatistical data.
 9. The computer-readable media of claim 8, whereingenerating a second language model includes determining amaximum-likelihood solution associated with the alternative parses. 10.The computer-readable media of claim 8, wherein the first language modelis determined from an initial set of statistical data associated withthe training corpus.
 11. The computer-readable media of claim 8, whereinthe first set of statistical data is determined based on frequency,occurrence, proximity or word distance between words or elements in thecorpus or token.
 12. The computer-readable media of claim 8, wherein thefirst set of statistical data comprises probabilities associated withentity definitions.
 13. The computer-readable media of claim 8 furthercomprising: applying the second language model to determine a second setof alternative parses of the training corpus; determining a second setof statistical data associated with the second set of alternativestatistical parses; and generating a third language model based on thesecond set of statistical data.
 14. The computer-readable media of claim8, wherein a token comprises one or more words, phrases, or entities.15. The computer-readable media of claim 8 further comprising accessinga set of entity definitions comprising a weighted list of one or moreentities, and wherein determining a first set of statistical dataassociated with the first set of alternative parses includes determiningupdated weights for the set of entity definitions.
 16. A method foroptimizing a language model for a domain performed by a computing devicehaving a processor and a memory, the method comprising: receiving atraining corpus comprising one or more words; receiving a set of entitydefinitions comprising a weighted list of one or more entities;determining a first language model based on the training corpus and theset of entity definitions; for a number of iterations, each iterationusing an iteration language model: (a) utilizing the iteration languagemodel to determine a set of alternative parses of the corpus; (b)determining updated weights for the set of entity definitions; (c)generating an updated language model from the set of alternative parsesdetermined in step (a) and from the set of updated weights determined instep (b); and (d) determining a language model evaluation; wherein theiteration language model is the first language model for the firstiteration, and wherein the iteration language model is the updatedlanguage model determined in step (c) for each subsequent iteration; andwherein the number of iterations is determined based on the languagemodel evaluation.
 17. The method of claim 16, wherein evaluating thelanguage model comprises determining that the language model hasachieved convergence, and wherein the number of iterations is the numberof times steps (a) through (d) are performed until the language modelhas achieved convergence.
 18. The method of claim 16, wherein generatingan updated language model comprises determining statistical dataassociated with the set of alternative parses and updated entity weightsin the set of entity definitions.
 19. The method of claim 16, whereinthe training corpus is determined from one or more user query logs, SMSmessages, web documents, electronic libraries, books, user inputlibraries, or created samples.
 20. The method of claim 16, wherein oneor more thresholds is applied to control the size of the updatedlanguage model.